Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.

PurposeThis paper aims to provide a TQM framework that stresses continuous improvements in teaching as a plausible means of TQM implementation in higher education programs.Design/methodology/approachThe literature survey of the TQM philosophies and the comparative analysis of TQM adoption in industry versus higher education provide the theoretical and practical background for this work. The analysis of TQM in higher education was done considering various critical factors such as existing educational practices, the barriers of TQM and the return on investment (ROI) of TQM implementations. These explorations led to the development of a TQM framework that adopts Deming's wheel of Plan‐Do‐Check‐Act (PDCA) cycle for implementing continuous improvements in higher education programs.FindingsUnlike the scenario in industry, TQM philosophies have to be adapted suitably for a successful implementation in higher education. The proposed TQM framework with six core quality elements encompassing the seven‐step course evaluation process flow provides a systematic guideline for an effective and efficient implementation of TQM in higher education.Originality/valueThis paper fulfils the need for a systematic, feasible and cost‐effective TQM framework for higher education. The new seven‐step course evaluation process flow offers a practical guidance for academics to implement TQM in higher education programs.

No abstract

No abstract

No abstract